Online Anomaly Detection Under Markov Statistics With Controllable Type-I Error

We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a specified false alarm rate while maximizing the detection power. Regardless of whether the source is stationary or non-stationary, the proposed algorithm sequentially receives a time series and learns the nominal attributes -in the online setting- under possibly varying Markov statistics. Then, an anomaly is declared at a time instance, if the observations are statistically sufficiently deviant. Moreover, the proposed algorithm is remarkably versatile since it does not require parameter tuning to match the desired rates even in the case of strong non-stationarity. The presented study is the first to provide the online implementation of Neyman-Pearson (NP) characterization for the problem such that the NP optimality, i.e., maximum detection power at a specified fal...

• Type: Journal
• Year: 2016
• Where: TSP
• Authors: Huseyin Ozkan, Fatih Ozkan, Suleyman Serdar Kozat
• Keywords: TSP 2016